How to build a Neural Network to approximate the Q-function?
I had the same question when I first learned RL. The architectural design may depend on the task you're considering. Since you're moving from a tabular Q-learning to function approximation, I'm suspecting that you are considering a relatively small action space; in this case, you should use option 2 where the input is the state and the number of output nodes matches the number of available actions.
One main reason for this choice: when exploiting the learnt function (i.e. policy is $\arg \max_a Q(s,a)$), for option (1) you would have to repeatedly run a forward pass for all available actions, whereas option (2) requires a single forward pass and run arg max either through numpy or the framework you're using.